What is the python code to reproduce Deepseek R1?

The key points of Deepseek R1 Research Paper

Proximal Policy Optimization (PPO) is one of the most powerful reinforcement learning algorithms, balancing stability and efficiency. This article breaks down how AI gradually improves in decision-making using trial, error, and strategic policy updates—just like learning to ride a bike!

The REINFORCE algorithm is the most basic policy gradient reinforcement learning algorithm. Imagine you’re learning to ride a bicycle without a teacher to guide you on what to do. You can only learn through "try → see the result → adjust → try again." The REINFORCE algorithm is the mathematical expression of this learning process.

Imagine you're playing a game of chess, and there are many choices at each step. Monte Carlo Tree Search is like a smart assistant that helps you find the best move by "simulating the future.”

A2C (Advantage Actor-Critic) is essentially an upgrade of REINFORCE.